Background Various behavioral sensing research studies have found that depressive symptoms are associated with human-smartphone interaction behaviors, including lack of diversity in unique physical locations, entropy of time spent in each location, sleep disruption, session duration, and typing speed. These behavioral measures are often tested against the total score of depressive symptoms, and the recommended practice to disaggregate within- and between-person effects in longitudinal data is often neglected. Objective We aimed to understand depression as a multidimensional process and explore the association between specific dimensions and behavioral measures computed from passively sensed human-smartphone interactions. We also aimed to highlight the nonergodicity in psychological processes and the importance of disaggregating within- and between-person effects in the analysis. Methods Data used in this study were collected by Mindstrong Health, a telehealth provider that focuses on individuals with serious mental illness. Depressive symptoms were measured by the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) Self-Rated Level 1 Cross-Cutting Symptom Measure-Adult Survey every 60 days for a year. Participants’ interactions with their smartphones were passively recorded, and 5 behavioral measures were developed and were expected to be associated with depressive symptoms according to either theoretical proposition or previous empirical evidence. Multilevel modeling was used to explore the longitudinal relations between the severity of depressive symptoms and these behavioral measures. Furthermore, within- and between-person effects were disaggregated to accommodate the nonergodicity commonly found in psychological processes. Results This study included 982 records of DSM Level 1 depressive symptom measurements and corresponding human-smartphone interaction data from 142 participants (age range 29-77 years; mean age 55.1 years, SD 10.8 years; 96 female participants). Loss of interest in pleasurable activities was associated with app count (γ10=−0.14; P=.01; within-person effect). Depressed mood was associated with typing time interval (γ05=0.88; P=.047; within-person effect) and session duration (γ05=−0.37; P=.03; between-person effect). Conclusions This study contributes new evidence for associations between human-smartphone interaction behaviors and the severity of depressive symptoms from a dimensional perspective, and it highlights the importance of considering the nonergodicity of psychological processes and analyzing the within- and between-person effects separately.

Background One in five adults in the US experience mental illness and over half of these adults do not receive treatment. In addition to the access gap, few innovations have been reported for ensuring the right level of mental healthcare service is available at the right time for individual patients. Methods Historical observational clinical data was leveraged from a virtual healthcare system. We conceptualize mental healthcare services themselves as therapeutic interventions and develop a prototype computational framework to estimate their potential longitudinal impacts on depressive symptom severity, which is then used to assess new treatment schedules and delivered to clinicians via a dashboard. We operationally define this process as “session dosing”: 497 patients who started treatment with severe symptoms of depression between November 2020 and October 2021 were used for modeling. Subsequently, 22 mental health providers participated in a 5-week clinical quality improvement (QI) pilot, where they utilized the prototype dashboard in treatment planning with 126 patients. Results The developed framework was able to resolve patient symptom fluctuations from their treatment schedules: 77% of the modeling dataset fit criteria for using the individual fits for subsequent clinical planning where five anecdotal profile types were identified that presented different clinical opportunities. Based on initial quality thresholds for model fits, 88% of those individuals were identified as adequate for session optimization planning using the developed dashboard, while 12% supported more thorough treatment planning (e.g. different treatment modalities). In the clinical pilot, 90% of clinicians reported using the dashboard a few times or more per member. Although most clinicians (67.5%) either rarely or never used the dashboard to change session types, numerous other discussions were enabled, and opportunities for automating session recommendations were identified. Conclusions It is possible to model and identify the extent to which mental healthcare services can resolve depressive symptom severity fluctuations. Implementation of one such prototype framework in a real-world clinic represents an advancement in mental healthcare treatment planning; however, investigations to assess which clinical endpoints are impacted by this technology, and the best way to incorporate such frameworks into clinical workflows, are needed and are actively being pursued.

BACKGROUND Various behavioral sensing research studies have found depressive symptoms are associated with human-smartphone interaction behaviors, including lack of diversity of unique physical locations, entropy of time spent in each location, sleep disruption, session duration, and typing speed. These behavioral measures are often tested against a total score of depressive symptoms and the recommended practice to disaggregate within- and between-person effects in longitudinal data is often neglected. OBJECTIVE We aimed to understand depression as a multi-dimensional process and explore the association between specific dimensions and behavioral measures computed from passively sensed human-smartphone interactions. We also aimed to highlight the non-ergodicity in psychological processes and the importance of disaggregating the within- and between-person effects in the analysis. METHODS Data used in this paper were collected by Mindstrong Health, a telehealth provider that focuses on individuals with Serious Mental Illness (SMI). Depressive symptoms were measured by DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure – Adult every 60 days for a year. Participants’ interactions with their smartphones were passively recorded and five behavioral measures were developed and expected to associate with depressive symptoms according to either theoretical proposition or previous empirical evidence. Multilevel modeling was used to explore the longitudinal relations between severity of depressive symptom and these behavioral measures. Furthermore, within- and between-person effects were disaggregated to accommodate the non-ergodicity commonly found in psychological processes. RESULTS This study included 982 records of DSM Level 1 depressive symptom measurements and corresponding human-smartphone interaction data from 142 participants (age from 29 to 77 years old with a mean of 55.1 and SD of 10.8; of whom 30% are female). Anhedonia was significantly associated with app count (γ_10= -0.14, p =0.01, within-person effect), and marginally significant associations was found between anhedonia and nighttime smartphone use (γ_30= 0.07, p =0.10, within-person effect), typing time interval (γ_05= 0.88, p =0.07, within-person effect), and session duration (γ_04= -0.29, p =0.07, between-person effect). Depressed mood was significantly associated with typing time interval (γ_05= 0.88, p =0.04, within-person effect), and session duration (γ_05= -0.37, p =0.03, between-person effect). CONCLUSIONS This study contributes new evidence of associations between human-smartphone interaction behaviors and the severity of depressive symptoms from a dimensional perspective, and it also highlights the importance of considering the non-ergodicity of psychological processes and analyzing the within- and between-person effects respectively.

BACKGROUND Various behavioral sensing research studies have found depressive symptoms are associated with human-smartphone interaction behaviors, including lack of diversity of unique physical locations, entropy of time spent in each location, sleep disruption, session duration, and typing speed. These behavioral measures are often tested against a total score of depressive symptoms and the recommended practice to disaggregate within- and between-person effects in longitudinal data is often neglected. OBJECTIVE We aimed to understand depression as a multi-dimensional process and explore the association between specific dimensions and behavioral measures computed from passively sensed human-smartphone interactions. We also aimed to highlight the non-ergodicity in psychological processes and the importance of disaggregating the within- and between-person effects in the analysis. METHODS Data used in this paper were collected by Mindstrong Health, a telehealth provider that focuses on individuals with Serious Mental Illness (SMI). Depressive symptoms were measured by DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure – Adult every 60 days for a year. Participants’ interactions with their smartphones were passively recorded and five behavioral measures were developed and expected to associate with depressive symptoms according to either theoretical proposition or previous empirical evidence. Multilevel modeling was used to explore the longitudinal relations between severity of depressive symptom and these behavioral measures. Furthermore, within- and between-person effects were disaggregated to accommodate the non-ergodicity commonly found in psychological processes. RESULTS This study included 982 records of DSM Level 1 depressive symptom measurements and corresponding human-smartphone interaction data from 142 participants (AgeM = 55.1; of whom 30% are female). Anhedonia was significantly associated with app count (γ_10= -0.14, p =0.01, within-person), and marginally significant associations was found between anhedonia and nighttime smartphone use (γ_30= 0.07, p =0.10, within-person effect), typing time interval (γ_05= 0.88, p =0.07, within-person effect), and session duration (γ_04= -0.29, p =0.07, between-person effect). Depressed mood was significantly associated with typing time interval (γ_05= 0.88, p =0.04, within-person effect), and session duration (γ_05= -0.37, p =0.03, between-person effect). CONCLUSIONS This study contributes new evidence of associations between human-smartphone interaction behaviors and the severity of depressive symptoms from a dimensional perspective, and it also highlights the importance of considering the non-ergodicity of psychological processes and analyzing the within- and between-person effects respectively.